Web content (e.g., webpages) is developed and utilized to provide websites and applications that are available for public and private consumption. In addition, various types of web content are developed and implemented in customized programmatic platforms utilized by members of organizations (e.g. businesses or government agencies) for communication, information exchange, administration, and productivity. Further, most software products available for purchase by individuals or organizations include some web-based content that provides significant functional components of the software. Many of these websites, applications, platforms, and software products (“web-content offerings”) incorporate a user interface (“UI”) that includes pages with a mixture of text and multi-media such as images, videos, and audio files.
Development of the web-content offerings and respective UI can involve multiple iterations of web page testing to ensure intended content presents and implements consistently across different browsers, operating systems, and devices. Web page testing can include various types of visual and manual regressions, browser automations, and web page comparisons that require analyzing large numbers of screen shots of the various pages. An objective for development teams using one or more of these methods is to ensure the right text, embedded between various multi-media content, and any additional content appears within the web pages correctly and in correct respective locations.
Analysis of the screenshots, from a text standpoint, may require a search of the text in each screenshot corresponding to a web page of a web-content offering. Further, members of a development team may need to text search different parts of the web pages for different reasons. For example, a quality engineer may need to verify that expected texts for titles, captions, or labels display correctly on a multitude of web pages. At early stages of a development cycle, it is not possible to determine if the expected content is displayed correctly on a large number of web pages using the naked eye, and automated test cases are not normally available for verification purposes at this stage. The screenshot analysis therefore is tedious in current systems.
In another example, technical writers may need to search text in web screenshots to compare against a latest version of web UI in order to reflect changes in user manuals or other documents. Due to the volume of web pages that need to be reviewed, and the limitations of current text searching techniques, screen shot searches and subsequent document updates may not occur frequently. As a result, a web UT may progress through multiple changes that will not be identified by searching for keywords that are expected for earlier changes. Thus, some of these changes may be missed and not reflected in the next update of the corresponding manual or other document.
Other development team members could use screenshots as part of their respective processes. Program managers review UIs for various purposes and do not usually check the UI on live pages in browsers but prefer to leverage captured screenshots of the webpages. In addition, linguist reviewers often use web screenshots to more effectively check if expected translations are reflected on the latest build of a web-content offering.
Text searching web screenshots has been done using Optical Character Recognition (OCR), but this technique has several limitations. First, recognizing text characters in screenshots may be time consuming. Further, the task of recognizing text by this technique is made more difficult, and can require ever-increasing amounts of time and computing resources, by the fact that much of the text to be searched is mixed in or intertwined with multi-media content. Finally, and most importantly, the ability to recognize text characters using the OCR technique and extracting text graphically, is highly dependent on the quality of the image that is being analyzed. This means that the accuracy of text recognition—the degree to which OCR recognizes the existence of text and correctly identifies what the text includes—is a product of the quality of the image being analyzed.
Therefore, a need exists for systems and methods that enable accurate searching of screenshots of web content that is not dependent on the quality of the image of the screenshot being analyzed.